Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
  • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
  • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
  • G01S5/0252—Radio frequency fingerprinting
  • G01S5/02521—Radio frequency fingerprinting using a radio-map
  • G01S5/02524—Creating or updating the radio-map
  • G01S5/02525—Gathering the radio frequency fingerprints
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
  • H04W16/22—Traffic simulation tools or models
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W24/00—Supervisory, monitoring or testing arrangements
  • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Definitions

• the invention relates to a method for computer-aided learning of a reference map based on measurements of features of a radio network and a corresponding device. Furthermore, the invention relates to a method for the computer-aided localization of a mobile object and to a corresponding device. Moreover, the invention relates to a computer program product.
• the invention relates to the technical field of localization of mobile objects using features of a radio network.
• various An ⁇ sets are known as mobile objects can be located via the detection of characteristics of a radio network.
• the radio network is formed by a multiplicity of base stations which can transmit and / or receive radio signals.
• the base stations communicate with a transmitting and / or receiving unit in the mobile object to be located. From the characteristics of the exchanged radio signals, the position of the mobile object can be determined.
• Known methods use for localization the absolute transit time or running time differences of radio signals between the respective base stations and the mobile object.
• methods exist, given its. Round-Trip-Time USAGE ⁇ ie the propagation time of a radio signal from a mobile object toward the base station and back to the mobile object or from a base station to a mobile object and back to the base station.
• the arrival angle of radio signals at the receiver and the transmission angle of radio signals are used in the transmission, thereby locating objects.
• a localization of mobile objects takes place via the signal strength of radio signals.
• Known localization methods are further distinguished as to whether the localization is based on a so-called finger printing or by the evaluation of geometric properties between the mobile object and the base stations. The evaluation of geometric properties is based on tri- or multilateration.
• calibration data in the form of features of the radio network from various known reference positions are generally combined in one
• Training phase collected to then based on a pattern matching (English, pattern matching) by means of a comparison of corresponding measured features of the radio network with the features at the reference positions to locate an object.
• the individual reference positions form a so-called reference map, which is also referred to as a radio map.
• the reference card is initially initialized and is learned online in the context of localization by newly added measurement data.
• Such a method is described in the publication WO 2007/118518 AI.
• finger printing methods have a higher accuracy.
• they require time-consuming manual calibration phase in which a mobile object on ei ⁇ ner plurality of predetermined spatial locations to be located at these positions and the characteristics of the radio network to be measured.
• the object of the invention is to provide a method for computer-aided learning of a reference card based on measurements of features of a radio network, which is easy to implement without complex calibration and provides good localization results. This object is achieved by the independent claims. Further developments of the invention are defined in the dependent claims.
• the inventive method is used for computer-aided learning a reference card based on measurements of features of a radio network, wherein the radio network in a particularly preferred embodiment form a local radio network and in particular a wireless network.
• the measurements used for learning are performed such that for a mobile object, which communicates via the radio network with a plurality of base stations of the radio network, ektpositionen at a plurality of non ⁇ knew whether the mobile object respective feature vectors of the radio network are measured, and thereby a Measurement series is obtained from a plurality of temporally successive feature vectors for respective object positions at respective measurement times.
• the learning process can be carried out online after completion of the measurements or in parallel during the measurement.
• the to-learn Refe rence ⁇ card includes a plurality of spatial reference ⁇ Posi tions, the respective feature vectors of the radio network to be learned at the respective reference positions of the reference map based on the series of measurements. That is to say that those feature vectors are suitably estimated, which result when the mobile object is arranged at a corresponding reference position.
• the reference card is thus distinguished by corresponding feature vectors at the individual reference positions of the card.
• the respective object positions at which the feature vectors of the radio network were measured are estimated in a step a) based on a pattern matching which compares the respective feature vectors of the measurement series with the feature vectors at the reference positions of the reference map. It is assumed that an appropriate initialization of the reference card is provided at the beginning of the process, which is initially used in the pattern matching.
• encryption Driving for pattern matching are well known in the art and are commonly referred to as pattern matching. They are based on the fact that similarities or similarities between predetermined patterns, which according to the invention are present as feature vectors of a reference map , and measured feature vectors are identified and the object position is determined therefrom.
• a step b) of the inventive method are determined based on optimization of a cost function optimized estimated Whether ektpositionen, wherein one or more boundary conditions are taken into account in the optimization, which are defined by a predetermined motion model for the mobile object, wherein the predetermined Be ⁇ wegungsmodell using the temporal order of the points of measurement of the measurement series sets of one or more Beschränkun ⁇ gene for the object motion.
• updated feature vectors of the radio network are determined at the reference positions of the reference card by means of the optimized estimated object positions.
• the inventive method is characterized in that in the context of learning the reference map and the zeitli ⁇ che order of the measurement time points is taken into account, and flows in a ge ⁇ suitable motion model.
• the motion ⁇ model may take into account useful in the measurement series both restrictions with regard to the last movement of the mobile object and with regard to the future movement of the mobile object. Which improved from the optimization resulting estimated object positions then provide a good basis for Approximati ⁇ on the corresponding feature vectors at the reference positions of the reference card.
• an initial calibration of the reference card can be dispensed with. Rather, a suitable initialization of the reference card is sufficient. and by repeating the learning steps several times, an increasingly accurate reference map can be learned.
• the method according to the invention can be used for time-based measurements, in which the feature vectors of the radio network include entries as runtime-based variables, each of which depends on a runtime of one or more radio signals of the radio network.
• the duration of the radio signal or signals can be the one-way transit time of a radio signal between a respective base station and the mobile object.
• the term may be a two-way time (also referred to as round-trip time hereinafter) be a Funksig ⁇ Nals of the mobile object to a respective base station and from there back to the respective base station or the two-way travel time of a radio signal from a respective Ba ⁇ sisstation to the mobile object and from there back to the respective base station.
• the runtime-based variables depend on a transit time in the form of a transit time difference.
• the transit time difference is defined for respective pairs of base stations and represents the difference between a first and a second runtime, wherein the first runtime is the transit time of a radio signal between the mobile object and the one base station of the pair and the second runtime the transit time of a radio signal between represents the mobile object and the other base station of the pair.
• the runtime-based variables in the entries of the feature vectors of the radio network are so-called channel impulse responses. These can be detected for the above-described transit times or transit time differences and represent the amplitude of the radio signal detected in dependence on the transit time or transit time difference in response to a transmit pulse.
• the channel impulse response is in a respective entry of Feature vector the Kanalimpulsant ⁇ word for the duration difference for a respective pair of base stations. That is, over the corresponding amplitudes the radio signals detected at the base stations for different propagation time differences, the channel impulse response is detected.
• the reference map is initialized at the beginning of the process such that for each ⁇ stays awhile reference position, the channel impulse response for a JE whis pair of base stations by a normal distribution whose mean value is a transit time difference based on estimated distances between the respective reference position and the base stations of the respective pair.
• the normal distribution is preferably standardized in such a way that the integral over the normal distribution yields 1, so that the impulse responses can be understood as probabilities for the occurrence of certain transit time differences.
• a number of feature vectors are determined at reference positions with the greatest agreement with the measured feature vector at a respective object position, based on the reference position (s) for the number of feature vectors, in particular by averaging the reference positions, the respective object position is estimated.
• This method is based on the known search for the k nearest neighbors, where k corresponds to the number of feature vectors.
• the feature vectors of the reference map are refreshes ⁇ al instrument within the pattern matching in parallel by the feature vectors of the reference map for reference positions in a predetermined environment can be corrected by an estimated using the pattern matching object position in each case with a correction term , wherein the correction term of the difference between the feature vector at the estimated via the pattern matching estimated ektposition and the respective feature vector of the reference map to be corrected.
• a correction term the correction term of the difference between the feature vector at the estimated via the pattern matching estimated ektposition and the respective feature vector of the reference map to be corrected.
• the size of the correction term decreases with increasing distance of the respective reference position in the predetermined environment from the object position estimated via the pattern matching.
• the correction term preferably depends on a Gaussian function with center at the position estimated via the pattern matching.
• guide the process is dimensionally performed iteratively for learning of the reference map, in which after a number of updates of the feature vectors of the reference map in step a) the steps b) and c) are carried out and then step a) ⁇ is returned, if a predetermined abort criterion, such as a predetermined number of iterations, is not yet met.
• the predetermined movement model used in step b) is suitably adapted to the mobile object under consideration.
• the motion model sets a maximum velocity for the movement of the mobile object as a constraint. This maximum speed can then be suitably taken into account within the measurement series for successive measurement times in such a way that two estimated object positions at consecutive measurement times must not be further apart than is possible according to the maximum velocity of the mobile object.
• the cost function used in step b) is a sum of Gaussian functions, each of which is based on the difference in magnitude between the respective data item and the sample item. Equally estimated is the ect position and the optimized estimated ect position to be determined.
• step c) the updated feature vectors of the radio network at the reference positions of the reference card are determined such that the optimized estimated object position having the smallest distance to a respective reference position of the reference card is determined, and the feature vector at the respective reference position is replaced by the measured feature vector at the optimized estimated object position with the smallest distance to the respective reference position of the reference map.
• the updated feature vectors of the radio network at the reference positions of the reference card is determined such that, for a respective reference Posi ⁇ tion a weighted sum of measured feature vectors is determined on optimized estimated object positions, wherein the weighted sum represents the updated feature vector at the respective reference position and wherein the respective weights of the summands in the sum are the smaller the farther an estimated optimized object position is from the respective reference position.
• the weights of the weighted sum are modeled by Gaussian functions, wherein a respective Gaussian function depends on the relative distance between a respective reference position and the respective optimized estimated object position.
• step a) and / or step b) fixed points with fixed spatial positions are used.
• NEN and feature vectors taken into account wherein, in the measurement ⁇ row one or more Whether ektpositionen are pre-assigned fixed points and / or be associated with the measured feature vectors of the series of measurements fixed points, eg by a feature vector whose accordance with a feature vector of a respective fixed point over-writing a predetermined threshold ⁇ tet, is equated with the respective fixed point.
• the spatial positions of fixed points in step a) are treated as estimated object positions or treated as optimized estimated object positions in step b). That is, the spatial positions of the fixed points are considered given and not determined by means of an estimate in step a). Likewise, the fixed points in the optimization in step b) are not considered as free parameters.
• the measured feature vectors of the series of measurements being an example of a sol ⁇ chen heuristic in the detailed description is set forth are assigned to fixed points on a heuristic.
• the spatial positions of the fixed points correspond to the spatial positions of the base stations.
• the invention further comprises a method for the computer-aided localization of a mobile object based on such a learned reference map.
• a feature vector of the radio network at the site to Loka ⁇ l inconvenienceden object position is measured.
• the object position is subsequently determined.
• the invention further comprises a device for computer-aided learning of a reference map based on measurements of features of a radio network, wherein the measurements are made such that for a mobile object, which via the Radio network communicates with a plurality of base stations of the radio network, at a plurality of unknown ob ektpositionen of the mobile object respective feature vectors of the radio network are measured, and thereby a series of measurements from a plurality of temporally successive feature vectors for respective object positions is obtained at respective measurement times.
• the reference card in this case comprises a plurality of spatial reference positions and the apparatus includes a processing unit, with the operation of the device based on the measurement series, the respective feature vectors of the radio network at the respective reference positions, the reference card are learned with the inventive method described above.
• the invention further relates to a device for the computer-aided localization of a mobile object, based on a reference map, which is learned with the learning method described above.
• the device comprises a measuring and processing device with which a feature vector of the radio network is measured at the object position to be located and with which based on a pattern matching, which compares the measured feature vector with the feature vectors at the reference positions of the learned reference map, the object position is determined ,
• the invention further comprises a Computerprogrammpro ⁇ domestic product with a program stored on a machine-readable carrier, the program code for performing the method for learning a reference map or for the localization of a mobile object described above, according to the invention, when the program runs on a computer.
• Fig. 1 is a schematic representation of a physical environment, is explained with the aid of learning ⁇ Re ference card for reference positions in the spatial environment of the present invention
• FIG. 2 is a diagram illustrating by way of example the course of the channel impulse response used in an embodiment of the invention as an entry of feature vectors;
• Fig. 3 is a schematic representation showing the iterative
• FIG. 4 is a diagram showing results of the invention shown SEN process with other processes.
• Embodiments of the method according to the invention based on feature vectors of a radio network will now be described, which contain as entries so-called channel impulse responses for a transit time difference of radio signals between base stations of corresponding pairs of base stations of the radio network.
• the radio network is a WLAN network in which the base stations represent corresponding access points.
• a mobile Whether ⁇ ject to the corresponding standard of the radio network emits Funksig ⁇ dimensional whose channel impulse response as a function of the transit time difference for respective pairs of base stations is measured.
• the expected feature vectors are then determined for a multiplicity of spatial reference positions of a reference map, wherein the reference map can subsequently be used, for example, to localize a mobile object by so-called pattern matching.
• pattern matching the corresponding Merkmalsvek ⁇ tor for a mobile object at an unknown location is measured, followed by comparison of the measured feature vector with the respective feature vectors of the reference Positions is determined in a suitable manner the ektposition.
• Fig. 1 illustrates generally the operation of the invention shown SEN method for learning a reference map.
• a spatial surroundings R is shown in the shape of a right ⁇ square space in which a plurality of regularly spaced and known reference positions RP are included a reference map.
• the corresponding feature vectors at these reference positions are to be determined in the course of learning the reference map.
• the reference positions are indicated in FIG. 1 by corresponding crosses, with only two of the reference positions being denoted by reference symbol RP for reasons of clarity .
• the feature vector present at these positions is denoted by C RM .
• three base stations BS1, BS2 and BS3 of the radio network are indicated in FIG. 1, which communicate with a corresponding mobile object 0 at an object position OP, the feature vector C meas being measured for the object position OP.
• the method according to the invention is based on measurements within a predetermined measurement period in which the object 0 moves in the spatial environment R and the feature vectors C meas at the corresponding object positions OP are detected at a plurality of measurement times. These object positions are not known, ie the measurement is not a calibration process in the actual sense in which corresponding feature vectors are detected at known positions.
• a measured feature vector includes an entry for each possible pair of two Basissta ⁇ functions of the radio network, that is, in the scenario of FIG. 1, an entry for the pair of base stations BSl and BS2, for the pair of base stations BSl and BS3 and for the pair of Base stations BS2 and BS3.
• FIG. 1 An entry for the pair of base stations BSl and BS2, for the pair of base stations BSl and BS3 and for the pair of Base stations BS2 and BS3.
• FIG. 1 illustrates a corresponding measurement of a feature vector for the pair of base stations BS1 and BS2.
• the measurement is based on the transit time difference LD of radio signals between a first transit time LI from the object 0 to the base station. on BS1 and a second transit time L2 from the object 0 to the base station BS2.
• the sum of the respective amplitudes of the radio signals, which are detected by the respective Basissta ⁇ functions BS1 and BS2 is detected as a function of a distance and the corresponding propagation time difference between the base stations.
• the resultant curve in this case represents the so-called.
• Channel impulse response which is illustrated in ⁇ way of example in Fig. 2.
• the distance d is indicated along the abscissa, the different (multiplied by the Signalgeschwin speed) propagation time differences of the radio signals for a pair of base stations reproduces.
• the amplitude AP is plotted, which is composed of the amplitudes of the radio signals, which are to be received at each Ba sisstation of the pair for the respective distance.
• the channel impulse response is represented as a solid curve K1.
• the maximum value of the channel impulse response ent ⁇ usually speaks of the time difference for a clear line of sight connection between the base stations and the mobile object.
• the other maxima of the curve are usually caused by Mehrwe ⁇ geausbreitonne due to reflections.
• a further dashed curve K2 is also given ⁇ again. This curve is used as an initialization for the channel impulse responses at a reference position of the reference map, and their calculation will be described later.
• the individual steps of an embodiment of the inventive method for learning a reference map based on channel impulse responses for time differences for pairs of base stations will be explained in detail.
• the learning of the reference map is based on a measurement series from a plurality of feature vectors which have been measured for different measurement positions.
• the individual entries of the feature vector C are the channel impulse responses in the time domain, which are approximated by a 5 inverse Fourier transform of a channel estimation in the frequency domain.
• Each entry of the above vector containing the channel impulse response in the form of L Amp ⁇ lituden to corresponding propagation time differences 1-T, 2-T, LT, which in this sense represent support points of the continuous channel impulse response for corresponding propagation time differences and distances respectively
• a corresponding entry is made for a pair of base stations BSm and BSn follows:
• the discrete time interval T of the channel impulse response depends as ⁇ in from the measurement setup.
• the amplitude values of several measurements can be averaged around the actual position before the learning described below.
• the k-th reference position in the reference map is thus described by the following position vector: 'RM [RM' RM -
• the individual positions are preferably the vertices of an equidistant grid.
• To loading 5 are beginning for each of the K reference positions initialized feature vectors Cj ⁇ j of the reference map RM (RM of the method
• N denotes the number of base stations.
• This amplitude is reproduced by way of example for a channel impulse response as curve K2 in FIG. N designates the normal distribution with a suitable standard deviation ⁇ and a normalization factor a.
• the normalization factor is chosen such that the integral over the Normalvertei ⁇ system is 1, and thus the individual amplitude values can be interpreted as true ⁇ probabilities for a certain distance difference between two base stations.
• corresponding estimated object positions are now determined for all measured channel impulse responses with a pattern matching known per se (pattern matching).
• the quadratic difference between a measured feature vector C ⁇ eas and all entries Cj ⁇ of the reference map is determined as follows: y Nl NL
• a predetermined number of entries of the reference card with the shortest distances to the current measured feature vector is selected.
• an iterative learning is used which so-called.
• Dorgani ⁇ sierende cards engl. Seif Organizing Maps
• SOM learning provides an adaptation is of the per se known Kohonen Seif Organizing Maps.
• an optimization is used in Inventive ⁇ contemporary learning, which is a motion model of the mobile object in a suitable manner for taken into account the complete measurement series. This optimization is referred to below as "Complete Track Optimization" and abbreviated CTO.
• the CTO-process represents a Wesent ⁇ Liche component of the inventive method, which is guide combined form as described herein from the additional SOM learning. Particularly good results are obtained by combining these two learning methods.
• Fig. 3 shows an example of a flow chart, which represents the ith ⁇ rative learning based on a combination of the SOM learning and CTO-learning. It designates the rations Colour Ite ⁇ .
• a complete update of the corresponding feature vectors of the reference card is first Runaway ⁇ performs, based on the SOM learning.
• a check step A checks whether a predetermined number of iteration steps it_cto is reached. If this is not the case, the reference card is updated again based on the SOM learning (branch B2).
• a CTO-learning step is Runaway ⁇ leads (branch Bl). Subsequently, the SOM learning is again performed for a predetermined number of iterations it_cto. The iteration is finally terminated upon reaching a predetermined total number n of iteration steps (branch 10 B3).
• This position now functions as a center for an update process of the corresponding feature vectors of the reference map with reference positions in the vicinity of the corresponding pattern-matching determined object position.
• the so-called neighborhood error is calculated as a difference between the currently measured characteristic vector C ⁇ EAS and the individual feature vectors
• the updating described above can be iteratively repeated several times, wherein in each iteration, preferably in random order, all corresponding measuring times or measured feature vectors are considered. Each individual iteration always uses the newly calculated feature vectors of the reference card.
• the sequenced ⁇ tielle chronological sequence of measurements which t from the above-described measurement time point vector is meas shows in suitable for formulating an optimization problem using a predetermined motion model for the mobile object.
• the goal is to improve the individual ⁇ nen entries of the reference card in the context of the entire measurement series ⁇ and the associated tracks of the mobile object.
• the starting point of the CTO optimization is again the position vector of the object positions estimated via the pattern matching, which reads as follows:
• the motion model can be defined depending on the mobile object ge ⁇ suitable. In the embodiment described herein the motion model based on the assumption that the mobile object can move a maximum with a maximum Ge ⁇ speed v MM of, for example 2 m / s. Un ⁇ ter this assumption is a Kos ⁇ tenfunktion g used to optimize and set a (1-1) dimensional limitation radio ⁇ tion h. These functions depend on possible positions of the mobile object at the individual measurement times. These positions are defined by the following vector:
• the aim of the optimization is to injure the effect minimizing the above cost function g (J *) without the constraints of the motion model that the above Beschränkungsfunkti on ⁇ z, (P) is zero becomes smaller.
• the optimization problem just described can be solved with known from the prior art algorithms, such as the Active Set algorithm. By manually supplying gradients of both the cost function and the constraint function, the computation time for the optimization can be significantly reduced.
• the above positions P are initialized by means of a per se known particle filter to thereby improve the results.
• the just described Opti ⁇ optimization can be regarded as a kind of tracking algorithm in which the complete history and future of data ⁇ set of the series of measurements is taken into account.
• the position vector P opt is obtained, which contains as entries the corresponding optimized estimated positions for the individual measurement times.
• the closest opti mized ⁇ position from the vector P opt is determined as follows:
• C ⁇ jy is therefore the updated feature vector for Refe ⁇ ence position k of the reference card.
• the determination of an updated feature vector of Refe rence ⁇ map is not performed by replacing the feature vector with the closest measured feature vector but using a weighted sum, which is represented by the following equation:
• a denotes a normalization factor.
• the a posteriori probability in the above equation is directly available when using particle filters, but can also be modeled by Gaussian curves around the positions P opt as follows:
• fixed points are so-called guide form in a preferred.
• the fixed points can be manually set in advance in the context of measurements of the feature vectors thereby, that in this case, corresponding Mes ⁇ solutions predetermined spatial positions of fixed points are assigned. These spatial positions are then no longer changed in the subsequent optimizations.
• corresponding fixed points can also be automatically determined from the Messda ⁇ th.
• a suitable heuristic ⁇ tik used for automatic detection of these fixed points. An example of such a heuristic will be described below.
• a corresponding measured feature vector is assigned to a fixed point during learning if the quadratic difference between the feature vector of the measurement and the predetermined measurement at the fixed point is below a predetermined threshold d fix , ie if: r (cL s , c ⁇ ) ⁇ x o
• Measurements which are assigned to fixed points are not estimated in the context of the method described above, but are regarded as predetermined.
• the pattern matching is performed. averaged positions replaced by the fixed point positions pj ', which in the context of SOM learning leads in most cases to a correct position of the center of the update ⁇ surfaces. This replacement is also done as part of CTO optimization. In this case, the corresponding fixed-point position is no longer a free parameter of the optimization, so that after the optimization, this fixed-point position is not changed and is equivalent to an estimated optimized position ektposition.
• fixed points are determined based on a heuristic ge ⁇ Telss of such measured feature vectors are detected, which are close to a corresponding base station.
• ge ⁇ is an appropriate measurement position, that is closest to the base station n ⁇ :
• the runtime difference For each measurement (ie, for each measurement time i), it is determined how long the runtime difference is for each pair of base stations that the base station n contains. The transit time difference is then since ⁇ by derived from the channel impulse response that the maximum value of the channel impulse response is used as the transit time difference. The channel impulse responses are thus considered as probabilities for corresponding propagation time differences.
• the runtime differences are summed for each base station pair containing the base station n. In this case, a sign is taken into account in a suitable manner as to whether in the corresponding base station pair the considered base station n is the first and the second base station. As a result, the closer the point of the corresponding measurement is to the base station n currently under consideration, the greater the sum becomes.
• the measurement with the largest sum is selected.
• the ent ⁇ speaking measurement position is then set equal to the position of the base station n.
• Such measurements can be calculated in a computationally efficient way which are located at base stations. These measurements are considered as fixed points and equated with the spatial positio ⁇ nen of the base stations.
• the inventive method has been tested by the inventors basie ⁇ rend on simulations. 4 shows a diagram which compares the results of the method according to the invention with methods known per se. In this case, a reference map learned with the method according to the invention was used for the localization of a mobile object and compared with localization methods known per se.
• the abscissa of the diagram of Fig. 4 shows the localization error LE and along the ordinate the cumulative probability Pcum for the corresponding localization error. That is, the steeper the corresponding curves extend upwards, the better the corresponding localization.
• the curve CU1 shows a localization carried out on the basis of the inventively learned reference card by means of pattern matching.
• the curve CU2 shows a localization based on pattern matching with a manually calibrated reference map.
• the curve CU3 shows a prior art location without a reference map, in which, for each possible position of the mobile object, the corresponding channel impulse responses of the individual pairs of
• the curve CU4 shows a pattern matching, in which the reference card is not learned, but the reference card originally initialized via a Gaussian function is used.
• the method according to the invention is according to the illustrated curve CU1 the best.
• the method according to the invention leads to better localization results than the above-described method according to the prior art, which is represented by the curve CU3.
• the worst localization results are achieved, as indicated by the flat curve CU4.
• the embodiments of the method according to the invention described above have a number of advantages.
• the reference map learned with the method improves runtime-based localization systems without the need for additional calibration phases or other measurement data.
• the method does not involve the disadvantages of the manual calibration must be gemes ⁇ sen in which corresponding with a high effort at predetermined known spatial positions of feature vectors of the radio network.
• predetermined fixed points can be incorporated within the scope of the learning, which can also be detected automatically, for example based on a heuristic.

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• 2011
  • 2011-04-15 DE DE102011007486.4A patent/DE102011007486B4/de not_active Expired - Fee Related
• 2012
  • 2012-04-03 WO PCT/EP2012/056041 patent/WO2012139929A1/de active Application Filing

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